Graph Data Structures for Beginners

In this post, we are going to explore non-linear data structures like graphs. Also, we’ll cover the central concepts and typical applications.

You are probably using programs with graphs and trees. Let’s say for instance that you want to know the shortest path between your workplace and home, you can use graph algorithms to get the answer! We are going to look into this and other fun challenges.

In the previous post, we explore linear data structures like arrays, linked lists, sets, stacks and so on. This one builds on top of what we learned.

Graphs Basics

A graph is a data structure where a node can have zero or more adjacent elements.

The connection between two nodes is called edge. Nodes can also be called vertices.

The degree is the number of edges connected to a vertex. E.g., the purple vertex has a degree of 3 while the blue one has a degree of 1.

If the edges are bi-directional, then we have an undirected graph. But, if the edges have a direction, then we have a directed graph or di-graph for short. You can think of it as a one-way street (directed) or two-way street (undirected).

Vertex can have edges that go to itself (e.g., blue node), this is called self-loop.

A graph can have cycles which means that if you traverse through the node, you could get the same node more than once. The graph without cycles is called acyclic graph.

Also, acyclic undirected graphs are called tree. We are going to cover trees in depth in the next post.

Not all vertices have to be connected in the graph. You might have isolated nodes or even separated subgraphs. If all nodes have at least one edge, then we have a connected graph. When all nodes are connected to all other nodes, then we have a complete graph.

For a complete graph, each node should have #nodes - 1 edges. In the previous example we have 7 vertices, so each node has 6 edges.

Graph Applications

When edges have values/cost assigned to them, we say we have a weighted graph. If the weight is absent, we can assume it’s 1.

Weighted graphs have many applications depending on the domain where you need to solve a problem. To name a few:

Airline Traffic (image above)

Node/vertex = Airport

Edges = direct flights between two airports

Weight = miles between two airports

GPS Navigation

Node = road intersection

Edge = road

Weight = time required to go from one intersection to another

Networks routing

Node = server

Edge = data link

Weight = connection speed

In general, graphs have many real-world applications like:

Electronic circuits

Flight reservations

Driving directions

Telcom: Cell tower frequency planning

Social networks. E.g., Facebook uses a graph for suggesting friends

Recommendations: Amazon/Netflix uses graphs to make suggestions for products/movies

Graphs help to plan the logistics of delivering goods

We just learned the basics of graphs and some applications. Let’s cover how to represent graphs in JavaScript.

Representing graphs

There are two primary ways of representing graph:

Adjacency list

Adjacency Matrix

Let’s explain it with the following directed graph (digraph) as an example:

We digraph with 4 nodes. When a vertex has a link to itself (e.g. a) is called self-loop.

Adjacency Matrix

The adjacency matrix is one way of representing a graph using a two-dimensional array (NxN matrix). In the intersection of nodes, we add 1 (or other weight) if they are connected and 0 or - if they are not connected.

Using the same example as before, we can build the following adjacency matrix:

Adjacency Matrix

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a b c d ea 1 1 - - -b - - 1 - -c - - - 1 -d - 1 1 - -

As you can see, the matrix list all nodes horizontally and vertically. If there a few connections we called sparse graph if there are many connections (close to the max number of links) we called it dense graph. If all possible connections are reached, then we have a complete graph.

It’s important to notice that for undirected graphs the adjacency matrix will always be symmetrical by the diagonal. However, that’s not always the case on a digraph (like our example).

What is the time complexity of finding connections of two vertices?

Querying if two nodes are connected in an adjacency matrix is O(1).

What is the space complexity?

Storing a graph as an adjacency matrix has a space complexity of O(n2), where n is the number of vertices. Also, represented as O(|V|2)

What is the runtime to add a vertex?

The vertices are stored as a VxV matrix. So, every time a vertex is added, the matrix needs to be reconstructed to a V+1xV+1.

Adding a vertex on an adjacency matrix is O(|V|2)

What about getting the adjacent nodes?

Since the matrix has a VxV matrix, to get all the adjacent nodes to a given vertex, we would have to go to the node row and get all its edges with the other nodes.

In our previous example, let’s say we want all the adjacent nodes to b. We have to get the full row where b is with all the other nodes.

12

a b c d eb - - 1 - -

We have to visit all nodes so,

Getting adjacent nodes on an adjacency matrix is O(|V|)

Imagine that you need to represent Facebook network as a graph. You would have to create a matrix of 2 billion x 2 billion, where most of it would be empty! Nobody would know everybody else just a few thousands at most.

In general, we deal with sparse graphs so the matrix will waste a lot of space. That’s why in most implementation we would use an adjacency list rather than the matrix.

Adjacency List

Adjacency List is one of the most common ways to represent graphs. Each node has a list of all the nodes connected to it.

Graphs can be represented as an adjacency list using an Array (or HashMap) containing the nodes. Each of these node entries includes a list (array, linked list, set, etc.) that list its adjacent nodes.

For instance, in the graph above we have that a has a connection to b and also a self-loop to itself. In turn, b has a connection to c and so on:

Adjacency List

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a -> { a b }b -> { c }c -> { d }d -> { b c }

As you can imagine if you want to know if a node is connected to another node, you would have to go through the list.

Querying if two nodes are connected in an adjacency list is O(n), where n is the number of vertices. Also represented as O(|V|)

What about the space complexity?

Storing a graph as an adjacency list has a space complexity of O(n), where n is the sum of vertices and edges. Also, represented as O(|V| + |E|)

Adjacency List Graph HashMap Implementation

The adjacency list is the most common way of representing graphs. There are several ways to implement the adjacency list:

One of them is using a HashMap. The key is the value of the node, and the value is an array of adjacency.

Adjacency List as a Hashmap

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const graph = { a: ['a', 'b'], b: ['c'], c: ['d'], d: ['b', 'c']}

Graph usually needs the following operations:

Add and remove vertices

Add and remove edges

Adding and removing vertices involves updating the adjacency list.

Let’s say that we want to remove the vertex b. We could do delete graph['b'];, however, we still have to remove the references on the adjacency list on d and a.

Every time we remove a node, we would have to iterate through all the nodes’ list O(|V| + |E|). Can we do better? We will answer that soon, but first, let’s *implement our list in a more object-oriented way so we can swap implementations easily.

Adjacency List Graph OO Implementation

Let’s start with the Node class that holds the vertex’s value and its adjacent vertices. We can also have helper functions for adding and removing adjacent nodes from the list.

We add an edge from the source vertex to the destination. If we have an undirected graph, then we also add from target node to source since it’s bidirectional.

The runtime of adding an edge from a graph adjacency list is: O(1)

If we try to add an edge and the nodes don’t exist, we need to create them first. Let’s do that next!

Graph.addVertex

The way we create a node is that we add it to the this.nodes Map. The map store a key/value pair, where the key is the vertex’s value while the map value is the instance of the node class. Take a look at line 5-6:

As you can see, we are using a Queue where the first node in is also the first node to be visited (FIFO).

We are as well using JavaScript generators, notice the * in front of the function. This generator iterates one value at a time. That’s useful for large graphs (millions of nodes) because in most cases you don’t need to visit every single node.

As you can see the graph is the same on BFS and DFS, however, the order how the nodes were visited is very different. BFS went from 1 to 10 in that order, while DFS went as deep as it could on each node.

Graph Time and Space Complexity

We have seen some of the basic operations of a Graph. How to add and remove vertices and edges. Here’s a summary of what we have covered so far:

As you can see, an adjacency list is faster in almost all operations. The only action that the adjacency matrix will outperform the adjacency list is checking if a node is adjacent to other. However, if we change our implementation from Array to a HashSet, we can get it in constant time as well :)

Summary

As we saw, Graphs can help to model many real-life scenarios such as airports, social networks, internet and so on. We covered some of the most basic algorithms such as Breadth-First Search (BFS) and Depth-First Search (DFS). Also, we studied about implementations trade-offs such as adjacency list and matrix. Subscribe to my newsletter and don’t miss any of my posts, because there are many other applications that we are going to learn soon, such as finding the shortest path between nodes and different exciting graph algorithms!

Adrian Mejia is a full-stack web developer located in Boston.
Currently working at Cisco as a Software Engineer.
Adrian enjoys writing posts about programming and technology.
Also, he likes to travel ✈️ and biking 🚴‍. Find out more here.